Billions of dollars are lost by low application of ineffective training. Fast declination of training memory may contribute this loss. The present study uses theoretical examinations via a conceptual model to examine the relationship between training memory and transfer behaviour. Training design, training retention (training memory), and training transfer are the study variables. The study population, is the federal ministries in the United Arab Emirates (UAE), was assessed via random sampling. Data were collected by a cross-sectional approach via questionnaires. Back-translation (English to Arabic), a pre-test, and a pilot test were applied to ensure that any modifications of the questionnaire items were precise and effective. The study was analysed via PLS-SEM. Based on the results, all of the study’s hypotheses were accepted, and significant relationships were revealed between the study variables. Training design is highly correlated with training retention, i.e., a premium training design will lead to a high preservation of the knowledge and skills gained from the training programme. Due to the low correlation between training retention and training transfer, the training retention was considered a secondary contributor of applying training to the work environment. If mangers and practitioners tend to achieve successful training transfer, their efforts should concentrate on adopting modern training design techniques, which could sufficiently maintain the training memory and increase training transfer.

This study investigates variables in training transfer in the general education (school) sector of the United Arab Emirates (UAE) by hybridizing the established training transfer model and the theory of planned behavior (TPB). The hybridized model employs four variables: (i) supervisor support, (ii) training design, (iii) intention to transfer, and (iv) training transfer. This model is used to test nine hypotheses. The study sample comprised 225 employees from the UAE general education sector. Study participants (respondents to a questionnaire) were recruited by simple random sampling. The study questionnaire data was analyzed using Partial least squares structural equation modeling PLS-SEM. The study model had a good fit confirming a good fit of the hypothesized model to the empirical data. Eight out of nine hypotheses were accepted. The study is generally parallel with TPB. It demonstrates that intention to transfer has a dominant and central (mediating) influence on transfer process and transfer behavior. Remarkably, supervisor support is important only in the pre-training phase. For the UAE education sector to succeed in effective training transfer, supervisors must be properly trained to design training programs, particularly to enhance the trainee’s intention to apply training on the job. This study proved empirically that designing training is a critical influence of a trainee’s intention to apply training. Training design and intention to transfer are mediators and play a central role in promoting the training transfer process. Future studies should focus on including TPB and intention in the training transfer researches.

In real life, there is a relationship between a person’s intention and memory. In addition, both are crucial antecedents of behaviour. This study puts this concept under empirical analysis. Additionally, high loss of training memory (50% after 24 hours) is a critical problem. Therefore, a weak understanding of intention and memory unity (interchangeable relationship) would exaggerate the transfer behaviour problem. It should be noted that billions of dollars are lost because of the low training implications (transfer). In that context, the researchers raise the question of ‘what comes first: intention or memory?’ and conduct a holistic statistical analysis. They apply a quantitative method (self-report survey) to test five hypotheses of this study’s variables: (i) intention to transfer (behaviour), (ii) training retention (memory), (iii) training transfer (behaviour). The study participants are 425 (population = 52,000) governmental (ministries) employees. The researchers derive and adapt the study questionnaire from reliable resources. They apply statistical analysis using PLS-SEM – SmartPLS software 3.0. All five hypotheses are accepted. This shows a highly interchangeable role of intention and memory against behaviour. However, the results analysis reveals that intention comes first, with a prominent presence of memory. Practically, it is suitable to understand intention and memory in combination, especially in the design phase. This would enhance the professionalism of behaviour control and effectiveness. For the theoretical tendency of the current study, the managerial implication is challenging. However, it opens the door for other interested researchers to specify a clear and smart solution for this case. In addition, this study has several values. It reconciles two theories in different fields: transfer model (training) with theory of planned behaviour (psychology). Mainly, it empirically describes the relationship between the most important behaviour antecedents (intention and memory). It helps to solve two practical problems: low training implication and high loss of training memory.   

The United Arab Emirates (UAE) is encountering a scarcity of water resources. It is counting on innovation management to alleviate the situation. In that context, there is a need for a managerial framework for this subject. Therefore, the aim of the current study is to build up an innovative managerial model. To establish this model, we applied a convergent, parallel, mixed-methods design. The study participants (n = 42) consisted mostly of leaders and experts working for the main water institutions. We analysed the quantitative method via partial least squares structural equation modelling (PLS-SEM), a SmartPLS software. Qualitative method procedures were conducted starting from coding, categorising, obtaining themes, and lastly, the establishment of grounded theory. We obtained two rigid inputs (quantitative and qualitative models) for the last phase (mixed-methods analysis). The quantitative findings revealed a significant and robust relationship (t value = 26.6, p = 0.000, coefficient = 0.888, R2 = 0.788). The qualitative findings also produced a steady grounded theory. Both quantitative and qualitative models were crossed according to the ‘convergence coding matrix’ and ‘triangulation analysis protocol’. Ultimately, we built a holistic framework named ‘the UAE water innovation model’, consisting of 12 components (meta-themes). This model should be adopted as the main guide for innovation management and strategy in water public sector institutions. Globally, this model could be a significant contribution, and it would be applicable to any country in the world with the same arid environment as the UAE.